Digital image analysis of gunshot residue dimensional dispersion by computer vision method

2021 ◽  
Author(s):  
Ilker Kara ◽  
Ersan Tahillioglu
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Gunshot Residue

scholarly journals Guidelines for Identification of Top-down Cracks (TDC) in In-Service Flexible Pavements

2019 ◽  
Vol 271 ◽  
pp. 08004
Author(s):  
Nirmal Dhakal ◽  
Mohammad Bashar ◽  
Mostafa Elseifi
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Digital Image ◽  
Crack Width ◽  
Digital Image Analysis ◽  
Flexible Pavements ◽  
Outer Edge ◽  
Crack Identification ◽  
Top Down ◽  
Average Crack

The objective of this study was to establish guidelines to identify top-down cracking (TDC) in flexible pavements using digital image analysis and the characteristics of these cracks. Past studies indicated that the time after construction of the pavement and location of the cracks are key parameters to identify top-down cracking. The cracks were reported to appear on the wheel path or at the outer edge of the wheel path typically within 3 to 8 years of construction. In-service pavement sections were selected for analysis based on the parameters identified from the literature and computer-vision techniques were employed to investigate the geometric characteristics of these cracks. Based on the results of the analysis, the average crack width was observed to be 3 to 7 mm. With respect to the orientation of the crack, the cracks segments were mostly longitudinal with typical deviation of 20 degrees. The orientation and intensity characteristics of top-down cracks were found as useful features in crack identification.

An innovative digital image analysis approach to quantify the percentage of voids in mineral aggregates of bituminous mixtures

1998 ◽  
Vol 25 (6) ◽  
pp. 1041-1049 ◽  
Author(s):  
Mohammed Taleb Obaidat ◽  
Hashem R Al-Masaeid ◽  
Fouad Gharaybeh ◽  
Taisir S Khedaywi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Cross Sections ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Asphalt Mixture ◽  
Fine Aggregate ◽  
Bituminous Mixtures ◽  
Mineral Aggregates

The objective of this study was to examine the feasibility of using a semiautomated computer-vision system to quantify the percentage of voids in mineral aggregates (VMA%) of bituminous mixtures. The system used a hybrid procedure which utilized a digital image analysis scheme and a planimeter surveying instrument. Thirty-nine Marshall specimens were prepared using limestone and gravel aggregates. Values of VMA% were obtained using the ASTM conventional procedure and the computer-vision procedure. To compute VMA% using the computer-vision procedure, normal case photography with uniform scale images was used to map horizontal and vertical cross sections of Marshall specimens. Image domain measurements were corrected for distortion. Spatial filters and image processing operations were used to enhance the aggregate edges. Experimental results showed slight variations between VMA% computed using conventional and the computer-vision procedures. The average differences of VMA% between conventional and the computer-vision procedures were 0.81% and 0.006% for gravel and limestone specimens, respectively. Measurements of VMA% for limestone mixtures were more precise than those for gravel mixtures because of the angular edge shape of limestone particles. Variations in VMA% were due to the anisotropic properties of asphalt mixtures, aggregate distribution in the asphalt mixture, and different shapes of aggregates. Using the computer-vision-based technique, VMA% of horizontal and vertical cross sections were 50% consistent. The existence of fine aggregate in the asphalt mixture affected the accuracy potential of the developed system because a low-resolution camera was used. Increasing the camera resolution and automating the area computation of aggregate are expected to enhance the potential accuracy of the procedure. The proposed method for VMA quantification is anticipated to improve field quality control of hot-mix asphalt (HMA). The use of computer-vision technology with bituminous mixtures can open the doors to a wide variety of applications.Key words: bituminous mixtures, voids in mineral aggregate, computer vision, automation, image processing.

Digital Image Analysis of the Larynx

2000 ◽  
Vol 10 (2) ◽  
pp. 7-9
Author(s):  
Yaser Natour ◽  
Christine Sapienza ◽  
Mark Schmalz ◽  
Savita Collins
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals Digital Image Analysis of BAP-1 Accurately Predicts Uveal Melanoma Metastasis

2019 ◽  
Vol 8 (3) ◽  
pp. 11 ◽  
Author(s):  
Gustav Stålhammar ◽  
Thonnie Rose O. See ◽  
Stephen Phillips ◽  
Stefan Seregard ◽  
Hans E. Grossniklaus
Keyword(s):  
Image Analysis ◽  
Uveal Melanoma ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Melanoma Metastasis

Quantitative evaluation of elderly skin based on digital image analysis

2008 ◽  
Vol 14 (2) ◽  
pp. 192-200 ◽  
Author(s):  
Hiromasa Tanaka ◽  
Gojiro Nakagami ◽  
Hiromi Sanada ◽  
Yunita Sari ◽  
Hiroshi Kobayashi ◽  
...  
Keyword(s):  
Image Analysis ◽  
Quantitative Evaluation ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals In vitro validation of Digital Image Analysis Sequence (DIAS) for the assessment of the marginal fit of cement-retained implant-supported experimental crowns

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Aristeidis A. Villias ◽  
Stefanos G. Kourtis ◽  
Hercules C. Karkazis ◽  
Gregory L. Polyzois
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Confidence Level ◽  
Digital Image Analysis ◽  
Statistical Tests ◽  
Interobserver Reliability ◽  
Intraclass Correlation ◽  
Marginal Fit ◽  
Smallest Detectable Change ◽  
The Mean

Abstract Background The replica technique with its modifications (negative replica) has been used for the assessment of marginal fit (MF). However, identification of the boundaries between prosthesis, cement, and abutment is challenging. The recently developed Digital Image Analysis Sequence (DIAS) addresses this limitation. Although DIAS is applicable, its reliability has not yet been proven. The purpose of this study was to verify the DIAS as an acceptable method for the quantitative assessment of MF at cemented crowns, by conducting statistical tests of agreement between different examiners. Methods One hundred fifty-one implant-supported experimental crowns were cemented. Equal negative replicas were produced from the assemblies. Each replica was sectioned in six parts, which were photographed under an optical microscope. From the 906 standardized digital photomicrographs (0.65 μm/pixel), 130 were randomly selected for analysis. DIAS included tracing the profile of the crown and the abutment and marking the margin definition points before cementation. Next, the traced and marked outlines were superimposed on each digital image, highlighting the components’ boundaries and enabling MF measurements. One researcher ran the analysis twice and three others once, independently. Five groups of 130 measurements were formed. Intra- and interobserver reliability was evaluated with intraclass correlation coefficient (ICC). Agreement was estimated with the standard error of measurement (SEM), the smallest detectable change at the 95% confidence level (SDC95%), and the Bland and Altman method of limits of agreement (LoA). Results Measured MF ranged between 22.83 and 286.58 pixels. Both the intra- and interobserver reliability were excellent, ICC = 1 at 95% confidence level. The intra- and interobserver SEM and SDC95% were less than 1 and 3 pixels, respectively. The Bland–Altman analysis presented graphically high level of agreement between the mean measurement of the first observer and each of the three other observers’ measurements. Differences between observers were normally distributed. In all three cases, the mean difference was less than 1 pixel and within ± 3 pixels LoA laid at least 95% of differences. T tests of the differences did not reveal any fixed bias (P > .05, not significant). Conclusion The DIAS is an objective and reliable method able to detect and quantify MF at ranges observed in clinical practice.

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